Robust and Low-Complexity Cooperative Spectrum Sensing via Low-Rank Matrix Recovery in Cognitive Vehicular Networks

Table 1

Notations.

Variables	Explanation
PU	Primary user
SU	Secondary user
CVNs	Cognitive vehicular networks
CSS	Cooperative spectrum sensing
FC	Fusion center
ED	Energy detection
SSDF	spectrum sensing data falsification
EGC	Equal Gain Combining
ADMM	The proximal alternating direction method of multipliers
	The detection probability
	The false alarm probability
	the received signal at the th SU in one subband
	The preset threshold at the FC
	The detected energy at the th SU
	The detected energy calculated by EGC
	The received primary signal power at the th SU
	The noise power
	The channel response
	The decorrelation function
	The correlation function between two nodes
	The distance between the node and
	The set of vehicles
	The number of subband
	The number of samples in each subband
	The tradeoff parameter
	A constant ()
	The number of cooperative vehicles
	A constant ()
	The shrinkage operator
	The sensing data matrix at the FC
	The energy matrix
	The real occupancy state matrix
	The energy detector output matrix
	The noise matrix
	The corrupted data matrix
	The weighting matrix
	The unitary matrix
	The positive semi-definite diagonal matrix
	The unitary matrix
	The Lagrangian multiplier matrix